Most enterprise AI failures aren't model problems—they're data architecture problems. Vivek Vaidya, serial entrepreneur with 25+ years building enterprise software and current CTO/Co-founder of super{set}, explains why vector databases alone can't solve enterprise AI and why knowledge graphs are foundational for production systems. He breaks down the critical difference between augmented intelligence (AI proposes, human approves) versus full automation, details how governance layers must respect existing enterprise data policies, and reveals why non-deterministic LLM outputs create compliance nightmares that kill enterprise adoption.

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